Evaluating summarization and inference techniques for high energy physics applications

Abstract

With continuing developments in experimental high energy physics, more and more data is being produced for analysis. As the size of data sets grows, the runtime and computational requirements of traditional inference procedures can become intractable. The problem of scalable inference appears in many fields, and thus it is an area of continuous development in computer science. With the proliferation of improved methods for data summarization and inference, an increasingly large onus is placed on individual researchers to determine the most appropriate methods for their specific problems. This work outlines the fundamentals of inference in high energy physics to establish a common foundation for readers in physics and computer scientist. It continues on to present a new set of tools that is designed to be used by researchers to evaluate summarization and inference methods for use on customized problems. The work presents sample evaluation results that can be produced by this tool. Finally, the work outlines how the tool can be used by researchers and highlights potential directions of interest in the search for more efficient inference techniques to be used in the field of high energy physics.